diff --git a/dask_ml/datasets.py b/dask_ml/datasets.py
index a561ee0d5..fb2afb831 100644
--- a/dask_ml/datasets.py
+++ b/dask_ml/datasets.py
@@ -460,3 +460,47 @@ def make_classification_df(
         )
 
     return X_df, y_series
+
+
+def make_s_curve(
+    n_samples=100,
+    noise=0.0,
+    random_state=None,
+    chunks=None,
+):
+    """
+    Generate an S curve dataset.
+
+    Parameters
+    ----------
+    n_samples : int, default=100
+        The number of sample points on the S curve.
+    noise : float, default=0.0
+        The standard deviation of the gaussian noise.
+    random_state : int, RandomState instance or None, default=None
+        Determines random number generation for dataset creation. Pass an int
+        for reproducible output across multiple function calls.
+        See :term:`Glossary <random_state>`.
+    chunks : int
+        Number of rows per dask array block.
+    Returns
+    -------
+    X : dask.array of shape (n_samples, 3)
+        The points.
+    t : dask.array of shape (n_samples,)
+        The univariate position of the sample according to the main dimension
+        of the points in the manifold.
+    """
+    rng = dask_ml.utils.check_random_state(random_state)
+
+    t_scale = 3 * np.pi * 0.5
+    t = rng.uniform(low=-t_scale, high=t_scale, size=(n_samples), chunks=(chunks,))
+    X = da.empty(shape=(n_samples, 3), chunks=(chunks, 3), dtype="f8")
+    X[:, 0] = da.sin(t)
+    X[:, 1] = rng.uniform(low=0, high=2, size=n_samples, chunks=(chunks,))
+    X[:, 2] = da.sign(t) * (da.cos(t) - 1)
+
+    if noise > 0:
+        X += rng.normal(scale=noise, size=X.shape, chunks=X.chunks)
+
+    return X, t
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index d221e2963..b90979441 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -55,6 +55,7 @@ def test_make_regression():
         dask_ml.datasets.make_classification,
         dask_ml.datasets.make_counts,
         dask_ml.datasets.make_regression,
+        dask_ml.datasets.make_s_curve,
     ],
 )
 def test_deterministic(generator, scheduler):
@@ -80,3 +81,19 @@ def test_make_classification_df():
     assert len(X_df) == 100
     assert len(y_series) == 100
     assert isinstance(y_series, dask.dataframe.core.Series)
+
+
+def test_make_s_curve():
+    X, X_color = dask_ml.datasets.make_s_curve(
+        n_samples=200,
+        random_state=0,
+        chunks=100,
+    )
+
+    assert isinstance(X, da.Array)
+    assert X.shape == (200, 3)
+    assert X.compute().shape == X.shape
+
+    assert isinstance(X_color, da.Array)
+    assert X_color.shape == (200,)
+    assert X_color.compute().shape == X_color.shape