As far as I've seen, opinions tend to differ about this. Best practice would certainly dictate using cross-validation (especially if comparing RFs with other algorithms on the same dataset). On the other hand, the original source states that the fact OOB error is calculated during model training is enough of an indicator of test set performance. Even Trevor Hastie, in a relatively recent talks says that "Random Forests provide free cross-validation". Intuitively, this makes sense to me, if training and trying to improve a RF-based model on one dataset.
Can someone please lay out the arguments for and against the need for cross-validation with random forests?