ROC vs precision-and-recall curves

I understand the formal differences between them, what I want to know is when it is more relevant to use one vs. the other. Do they always provide complementary insight about the performance of a given classification/detection system? When is it reasonable to provide them both, say, in a paper? instead of just one? Are there any alternative (maybe more modern) descriptors that capture the relevant aspects of both ROC and precision recall for a classification system? I am interested in arguments for both binary and multi-class (e.g. as one-vs-all) cases.