diff --git a/tensorflow_privacy/privacy/keras_models/dp_keras_model.py b/tensorflow_privacy/privacy/keras_models/dp_keras_model.py
index fd51cf6..414b865 100644
--- a/tensorflow_privacy/privacy/keras_models/dp_keras_model.py
+++ b/tensorflow_privacy/privacy/keras_models/dp_keras_model.py
@@ -20,7 +20,42 @@ def make_dp_model_class(cls):
   """Given a subclass of `tf.keras.Model`, returns a DP-SGD version of it."""
 
   class DPModelClass(cls):  # pylint: disable=empty-docstring
-    __doc__ = ('DP subclass of `tf.keras.{}`.').format(cls.__name__)
+    __doc__ = ("""DP subclass of `{base_model}`.
+
+       This can be used as a differentially private replacement for
+       {base_model}. This class implements DP-SGD using the standard
+       Gaussian mechanism.
+
+       When instantiating this class, you need to supply several
+       DP-related arguments followed by the standard arguments for
+       `{short_base_model}`.
+
+       Examples:
+
+       ```python
+       # Create Model instance.
+       model = {dp_model_class}(l2_norm_clip=1.0, noise_multiplier=0.5, use_xla=True,
+                <standard arguments>)
+       ```
+
+       You should use your {dp_model_class} instance with a standard instance
+       of `tf.keras.Optimizer` as the optimizer, and a standard reduced loss.
+       You do not need to use a differentially private optimizer.
+
+       ```python
+       # Use a standard (non-DP) optimizer.
+       optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
+
+       # Use a standard reduced loss.
+       loss = tf.keras.losses.MeanSquaredError()
+
+       model.compile(optimizer=optimizer, loss=loss)
+       model.fit(train_data, train_labels, epochs=1, batch_size=32)
+       ```
+
+       """).format(base_model='tf.keras.' + cls.__name__,
+                   short_base_model=cls.__name__,
+                   dp_model_class='DP' + cls.__name__)
 
     def __init__(
         self,