What is ROC-AUC and how do you interpret it?

The ROC curve plots the true positive rate (recall) against the false positive rate (FP/(FP+TN)) across all classification thresholds. AUC is the area under this curve, ranging from 0 to 1. An AUC of 0.5 means the model is no better than random guessing, and 1.0 is perfect separation. Intuitively, AUC equals the probability that the model ranks a randomly chosen positive example higher than a randomly chosen negative one, so it measures ranking quality independent of any single threshold. It is threshold-agnostic and useful for comparing models. A caveat: on highly imbalanced datasets ROC-AUC can look optimistic because the false positive rate stays low; in that case the precision-recall curve and PR-AUC are more informative.