Comparing resampling methods

What are the advantages and disadvantages of using one resampling method over another for training models to avoid overfitting?

I ask because I've recently started using the amazing caret package for R and wasn't quite sure which method to use for my resampling with the train function: boot, boot632, cv, repeatedcv, LOOCV, LGOCV, or oob.

Does anybody know what main differences between these methods are? For example, I'm confused about what the difference between cv and repeatedcv is.