diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py
index 0ee4a91f417..9e30e3610ec 100644
--- a/tensorflow/python/keras/activations.py
+++ b/tensorflow/python/keras/activations.py
@@ -86,18 +86,49 @@ def softmax(x, axis=-1):
 @keras_export('keras.activations.elu')
 @dispatch.add_dispatch_support
 def elu(x, alpha=1.0):
-  """Exponential linear unit.
+  """Exponential Linear Unit.
+
+  The exponential linear unit (ELU) with `alpha > 0` is:
+  `x` if `x > 0` and
+  `alpha * (exp(x) - 1)` if `x < 0`
+  The ELU hyperparameter `alpha` controls the value to which an
+  ELU saturates for negative net inputs. ELUs diminish the
+  vanishing gradient effect.
+
+  ELUs have negative values which pushes the mean of the activations
+  closer to zero.
+  Mean activations that are closer to zero enable faster learning as they
+  bring the gradient closer to the natural gradient.
+  ELUs saturate to a negative value when the argument gets smaller.
+  Saturation means a small derivative which decreases the variation
+  and the information that is propagated to the next layer.
+
+  Example Usage:
+
+  >>> import tensorflow as tf
+  >>> model = tf.keras.Sequential()
+  >>> model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',
+  ...          input_shape=(28, 28, 1)))
+  >>> model.add(tf.keras.layers.MaxPooling2D((2, 2)))
+  >>> model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu'))
+  >>> model.add(tf.keras.layers.MaxPooling2D((2, 2)))
+  >>> model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu'))
+
+  <tensorflow.python.keras.engine.sequential.Sequential object ...>
 
   Arguments:
       x: Input tensor.
-      alpha: A scalar, slope of negative section.
+      alpha: A scalar, slope of negative section. `alpha` controls the value to
+        which an ELU saturates for negative net inputs.
 
   Returns:
-      The exponential linear activation: `x` if `x > 0` and
-        `alpha * (exp(x)-1)` if `x < 0`.
+      The exponential linear unit (ELU) activation function: `x` if `x > 0` and
+      `alpha * (exp(x) - 1)` if `x < 0`.
+
 
   Reference:
-      - [Clevert et al. 2016](https://arxiv.org/abs/1511.07289)
+      [Fast and Accurate Deep Network Learning by Exponential Linear Units
+      (ELUs) (Clevert et al, 2016)](https://arxiv.org/abs/1511.07289)
   """
   return K.elu(x, alpha)