diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index 92172bbf104..135afb04e5d 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -97,9 +97,6 @@ class BaseEstimator(sklearn.BaseEstimator):
     * _get_predict_ops
 
   `Estimator` implemented below is a good example of how to use this class.
-
-  Parameters:
-    model_dir: Directory to save model parameters, graph and etc.
   """
   __metaclass__ = abc.ABCMeta
 
@@ -107,6 +104,12 @@ class BaseEstimator(sklearn.BaseEstimator):
   _Config = run_config.RunConfig  # pylint: disable=invalid-name
 
   def __init__(self, model_dir=None, config=None):
+    """Initializes a BaseEstimator instance.
+
+    Args:
+      model_dir: Directory to save model parameters, graph and etc.
+      config: A RunConfig instance.
+    """
     # Model directory.
     self._model_dir = model_dir
     if self._model_dir is None:
@@ -621,29 +624,6 @@ class BaseEstimator(sklearn.BaseEstimator):
 
 class Estimator(BaseEstimator):
   """Estimator class is the basic TensorFlow model trainer/evaluator.
-
-  Parameters:
-    model_fn: Model function, takes features and targets tensors or dicts of
-              tensors and returns predictions and loss tensors.
-              Supports next three signatures for the function:
-                * `(features, targets) -> (predictions, loss, train_op)`
-                * `(features, targets, mode) -> (predictions, loss, train_op)`
-                * `(features, targets, mode, params) ->
-                    (predictions, loss, train_op)`
-              Where:
-                * `features` are single `Tensor` or `dict` of `Tensor`s
-                     (depending on data passed to `fit`),
-                * `targets` are `Tensor` or
-                    `dict` of `Tensor`s (for multi-head model).
-                * `mode` represents if this training, evaluation or prediction.
-                    See `ModeKeys` for example keys.
-                * `params` is a `dict` of hyperparameters. Will receive what is
-                    passed to Estimator in `params` parameter. This allows to
-                    configure Estimators from hyper parameter tunning.
-    model_dir: Directory to save model parameters, graph and etc.
-    config: Configuration object.
-    params: `dict` of hyper parameters that will be passed into `model_fn`.
-            Keys are names of parameters, values are basic python types.
   """
 
   def __init__(self,
@@ -651,6 +631,34 @@ class Estimator(BaseEstimator):
                model_dir=None,
                config=None,
                params=None):
+    """Constructs an Estimator instance.
+
+    Args:
+      model_fn: Model function, takes features and targets tensors or dicts of
+                tensors and returns predictions and loss tensors.
+                Supports next three signatures for the function:
+                  * `(features, targets) -> (predictions, loss, train_op)`
+                  * `(features, targets, mode) -> (predictions, loss, train_op)`
+                  * `(features, targets, mode, params) ->
+                      (predictions, loss, train_op)`
+                Where:
+                  * `features` are single `Tensor` or `dict` of `Tensor`s
+                       (depending on data passed to `fit`),
+                  * `targets` are `Tensor` or
+                      `dict` of `Tensor`s (for multi-head model).
+                  * `mode` represents if this training, evaluation or
+                      prediction. See `ModeKeys` for example keys.
+                  * `params` is a `dict` of hyperparameters. Will receive what
+                      is passed to Estimator in `params` parameter. This allows
+                      to configure Estimators from hyper parameter tunning.
+      model_dir: Directory to save model parameters, graph and etc.
+      config: Configuration object.
+      params: `dict` of hyper parameters that will be passed into `model_fn`.
+              Keys are names of parameters, values are basic python types.
+
+    Raises:
+      ValueError: parameters of `model_fn` don't match `params`.
+    """
     super(Estimator, self).__init__(model_dir=model_dir, config=config)
     if model_fn is not None:
       # Check number of arguments of the given function matches requirements.
diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 258cf38b9f2..f5fe4dfb7c8 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -21,14 +21,16 @@ Concrete implementation of this class should provide following functions:
   * _get_predict_ops
 
 `Estimator` implemented below is a good example of how to use this class.
-
-Parameters:
-  model_dir: Directory to save model parameters, graph and etc.
 - - -
 
 #### `tf.contrib.learn.BaseEstimator.__init__(model_dir=None, config=None)` {#BaseEstimator.__init__}
 
+Initializes a BaseEstimator instance.
 
+##### Parameters:
+
+
+*  <b>`model_dir`</b>: Directory to save model parameters, graph and etc.
 
 
 - - -
@@ -270,16 +272,23 @@ component of a nested object.
 ### `class tf.contrib.learn.Estimator` {#Estimator}
 
 Estimator class is the basic TensorFlow model trainer/evaluator.
+- - -
 
-Parameters:
-  model_fn: Model function, takes features and targets tensors or dicts of
+#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__}
+
+Constructs an Estimator instance.
+
+##### Args:
+
+
+*  <b>`model_fn`</b>: Model function, takes features and targets tensors or dicts of
             tensors and returns predictions and loss tensors.
             Supports next three signatures for the function:
               * `(features, targets) -> (predictions, loss, train_op)`
               * `(features, targets, mode) -> (predictions, loss, train_op)`
               * `(features, targets, mode, params) ->
                   (predictions, loss, train_op)`
-            Where:
+*  <b>`Where`</b>: 
               * `features` are single `Tensor` or `dict` of `Tensor`s
                    (depending on data passed to `fit`),
               * `targets` are `Tensor` or
@@ -289,15 +298,10 @@ Parameters:
               * `params` is a `dict` of hyperparameters. Will receive what is
                   passed to Estimator in `params` parameter. This allows to
                   configure Estimators from hyper parameter tunning.
-  model_dir: Directory to save model parameters, graph and etc.
-  config: Configuration object.
-  params: `dict` of hyper parameters that will be passed into `model_fn`.
+*  <b>`model_dir`</b>: Directory to save model parameters, graph and etc.
+*  <b>`config`</b>: Configuration object.
+*  <b>`params`</b>: `dict` of hyper parameters that will be passed into `model_fn`.
           Keys are names of parameters, values are basic python types.
-- - -
-
-#### `tf.contrib.learn.Estimator.__init__(model_fn=None, model_dir=None, config=None, params=None)` {#Estimator.__init__}
-
-
 
 
 - - -