Consider a scenario where the dataset in hand is quite large, let's assume 50000 samples (quite well balanced between two classes). What metrics can be used to decide the K value in a K-fold cross-validation? In other words, can a 5-fold CV be enough or should I go for a 10-fold CV?
The rule of thumb is the higher K, the better. But, putting aside the computational costs, what can be used to decide the value of K? Should we look at the overall performance, e.g. average accuracy? That is, if accuracy (5CV) ~ accuracy(10CV), we can opt for 5-fold CV?. Is the standard deviation between the performance of different folds important? That is, the lower the better?