diff --git a/doc/whats_new/v0.20.rst b/doc/whats_new/v0.20.rst
index b374e66e7b25e..fb1a06aa3f04a 100644
--- a/doc/whats_new/v0.20.rst
+++ b/doc/whats_new/v0.20.rst
@@ -172,6 +172,11 @@ Model evaluation and meta-estimators
   group-based CV strategies. :issue:`9085` by :user:`Laurent Direr <ldirer>`
   and `Andreas Müller`_.
 
+- The ``predict`` method of :class:`pipeline.Pipeline` now passes keyword
+  arguments on to the pipeline's last estimator, enabling the use of parameters
+  such as ``return_std`` in a pipeline with caution.
+  :issue:`9304` by :user:`Breno Freitas <brenolf>`.
+
 - Add `return_estimator` parameter in :func:`model_selection.cross_validate` to
   return estimators fitted on each split. :issue:`9686` by :user:`Aurélien Bellet
   <bellet>`.
diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
index 796de4d5543ba..67649d76d428a 100644
--- a/sklearn/pipeline.py
+++ b/sklearn/pipeline.py
@@ -287,7 +287,7 @@ def fit_transform(self, X, y=None, **fit_params):
             return last_step.fit(Xt, y, **fit_params).transform(Xt)
 
     @if_delegate_has_method(delegate='_final_estimator')
-    def predict(self, X):
+    def predict(self, X, **predict_params):
         """Apply transforms to the data, and predict with the final estimator
 
         Parameters
@@ -296,6 +296,14 @@ def predict(self, X):
             Data to predict on. Must fulfill input requirements of first step
             of the pipeline.
 
+        **predict_params : dict of string -> object
+            Parameters to the ``predict`` called at the end of all
+            transformations in the pipeline. Note that while this may be
+            used to return uncertainties from some models with return_std
+            or return_cov, uncertainties that are generated by the
+            transformations in the pipeline are not propagated to the
+            final estimator.
+
         Returns
         -------
         y_pred : array-like
@@ -304,7 +312,7 @@ def predict(self, X):
         for name, transform in self.steps[:-1]:
             if transform is not None:
                 Xt = transform.transform(Xt)
-        return self.steps[-1][-1].predict(Xt)
+        return self.steps[-1][-1].predict(Xt, **predict_params)
 
     @if_delegate_has_method(delegate='_final_estimator')
     def fit_predict(self, X, y=None, **fit_params):
diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py
index ab2108ed690f2..95172b103be7a 100644
--- a/sklearn/tests/test_pipeline.py
+++ b/sklearn/tests/test_pipeline.py
@@ -144,6 +144,17 @@ def fit(self, X, y):
         return self
 
 
+class DummyEstimatorParams(BaseEstimator):
+    """Mock classifier that takes params on predict"""
+
+    def fit(self, X, y):
+        return self
+
+    def predict(self, X, got_attribute=False):
+        self.got_attribute = got_attribute
+        return self
+
+
 def test_pipeline_init():
     # Test the various init parameters of the pipeline.
     assert_raises(TypeError, Pipeline)
@@ -398,6 +409,16 @@ def test_fit_predict_with_intermediate_fit_params():
     assert_false('should_succeed' in pipe.named_steps['transf'].fit_params)
 
 
+def test_predict_with_predict_params():
+    # tests that Pipeline passes predict_params to the final estimator
+    # when predict is invoked
+    pipe = Pipeline([('transf', Transf()), ('clf', DummyEstimatorParams())])
+    pipe.fit(None, None)
+    pipe.predict(X=None, got_attribute=True)
+
+    assert_true(pipe.named_steps['clf'].got_attribute)
+
+
 def test_feature_union():
     # basic sanity check for feature union
     iris = load_iris()