KNN pipeline w/ cross_validation_scores
Your usage (and your code) is actually fine, which means there isn't much to review.

The only tip I would give is that having only the mean of the cross validation scores is not enough to determine if your model did well. Imagine, for instance, that you have 4 cv that gave the following accuracy scores : [0.01, 0.90, 0.90, 0.90] / 4 = 0.677 vs another set of hyper-parameters that gave [0.77, 0.79, 0.76, 0.80] / 4 = 0.78. That's a fairly crazy example, because such a big gap in the first cv scores is unrealistic, but it's only an example. The mean isn't very robust to outliers, that's what I mean. In order to have more information, it's great to also keep tracking of the standard deviation. You can also plot it using errorbar in matplotlib.