How to gauge overfit with MLPClassifier and cross_val_score?
If I understand your question correctly, you would like the algorithm to detect overfitting by itself, without you manual inspection.

For this you need another split of data. So first you split your entire dataset into training and test, and then you split again your training into two subsets: the actual training and validation. You use these two subsets internally during the learning process to estimate accuracy and detect overfitting (and you may use cross_val_score() for this purpose if you want, just do not touch the holdout test part).

On a side note, looking at your two loops (for activation and neuron), you may want to use Grid Search for this.