sklearn.model_selection.cross_validate fits and scores several models over some CV splits of data.

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html

Users can currently do this distributed on a cluster with

import dask_ml.joblib

with joblib.parallel_backend('dask', scatter=[X, y]):
    sklearn.model_selection.cross_validate(estimator, X, y)

Why not do that for them by defining a dask_ml.model_selection.cross_validate that does the parallel_backend and scattering for them?