diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py
index e09d2d09d7e43..a96ca4182856b 100644
--- a/sklearn/compose/_column_transformer.py
+++ b/sklearn/compose/_column_transformer.py
@@ -669,6 +669,14 @@ def make_column_transformer(*transformers, **kwargs):
         non-specified columns will use the ``remainder`` estimator. The
         estimator must support `fit` and `transform`.
 
+    sparse_threshold : float, default = 0.3
+        If the transformed output consists of a mix of sparse and dense data,
+        it will be stacked as a sparse matrix if the density is lower than this
+        value. Use ``sparse_threshold=0`` to always return dense.
+        When the transformed output consists of all sparse or all dense data,
+        the stacked result will be sparse or dense, respectively, and this
+        keyword will be ignored.
+
     n_jobs : int or None, optional (default=None)
         Number of jobs to run in parallel.
         ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
@@ -705,9 +713,11 @@ def make_column_transformer(*transformers, **kwargs):
     """
     n_jobs = kwargs.pop('n_jobs', None)
     remainder = kwargs.pop('remainder', 'drop')
+    sparse_threshold = kwargs.pop('sparse_threshold', 0.3)
     if kwargs:
         raise TypeError('Unknown keyword arguments: "{}"'
                         .format(list(kwargs.keys())[0]))
     transformer_list = _get_transformer_list(transformers)
     return ColumnTransformer(transformer_list, n_jobs=n_jobs,
-                             remainder=remainder)
+                             remainder=remainder,
+                             sparse_threshold=sparse_threshold)
diff --git a/sklearn/compose/tests/test_column_transformer.py b/sklearn/compose/tests/test_column_transformer.py
index f67806a52c543..4304b9184e923 100644
--- a/sklearn/compose/tests/test_column_transformer.py
+++ b/sklearn/compose/tests/test_column_transformer.py
@@ -431,11 +431,13 @@ def test_make_column_transformer_kwargs():
     scaler = StandardScaler()
     norm = Normalizer()
     ct = make_column_transformer(('first', scaler), (['second'], norm),
-                                 n_jobs=3, remainder='drop')
+                                 n_jobs=3, remainder='drop',
+                                 sparse_threshold=0.3)
     assert_equal(ct.transformers, make_column_transformer(
         ('first', scaler), (['second'], norm)).transformers)
     assert_equal(ct.n_jobs, 3)
     assert_equal(ct.remainder, 'drop')
+    assert_equal(ct.sparse_threshold, 0.3)
     # invalid keyword parameters should raise an error message
     assert_raise_message(
         TypeError,