ROC curves are a graph that shows how sensitivity changes with different levels of specificity. They are useful for working out the best trade-off between sensitivity and specificity to suit the situation. Or in other words, how best to balance the probability of a true positive result against the probability of a false negative result. They are also useful for determining how useful a test or statistical model is. Jennifer Daddysman from University of Kentucky explains more in this video.
Jennifer Daddysman (2017) Sensitivity, Specificity, and ROC Curves, https://www.youtube.com/watch?v=Fn1t4rEIf_8