Getting the leave-one-out error on least square regression to fit polynomials
numpy and scikit-learn are quite separate open-source projects. As such, they are compatible in some ways but not in others. All scikit-learn models are variants on the fundamental estimator object (see https://scikit-learn.org/stable/tutorial/statistical_inference/settings.html). The cross_val_score function expects a scikit-learn-style estimator object and there's no easy way to create such an object from the output of numpy.polyfit.

I'd suggest working entirely in scikit-learn, creating a regression estimator that you can cross-validate with cross_val_score. This SO post shows you how to build that polynomial regression estimator.