Oversampling before Cross-Validation, is it a problem?
Oversampling the training data may help the classifier to better predict on the originally less represented class. This does not mean that it should be applied to performance metrics, as it changes the original target distribution and thus creates bias in the results.

Imagine the problem of cancer detection, where your original dataset is unbalanced: 10% of the patients have cancer y=1 and the remaining 90% don't y=0. If you train a classifier which is prone to error on unbalanced datasets (such as an Artificial Neural Network), you may end up predicting always the majority class: y=0.

If you oversample to a new distribution, let's say 50/50,  your classifier is expected to increase the performance, specially on the positive class. Nonetheless, to measure the performance on real data, which is by itself skewed, measure on oversampled may not be the best choice.

Thus, if you are optimizing the hyperparameters or choosing from a set of classifiers, cross-validating with oversampled data may provide you with a different perspective on the classifier's ability to predict on both classes with equal importance. Nonetheless, if you are estimating the real-life prediction capability, I would not advise you to oversample such validation data!