Combine RepeatedStratifiedKFold and crossval
I found a recent paper dealing with repeated cross-validation: 

On estimating model accuracy with repeated cross-validation

It states: If $A_i$ is the result returned by a single k-fold crossvalidation, then
the cross-validation result for the whole repeated cross-validation (n repeats) is: $A = \frac{\sum_i A_i}{n}$. 

So to calculate the final score, we need a "chunks" function: (Code from stackoverflow)

def chunks(lst, n):
    means = []
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        means.append(np.mean(lst[i:i + n]))
    return means


Then the final score can be calculated by: 

np.mean(chunks(scores, n_splits))