The ROC curve is a graphical tool for evaluating the performance of a binary classifier across different discrimination thresholds. It plots the true positive rate (TPR) against the false positive rate (FPR) as the threshold for classifying positives is varied​.Each point on the ROC curve represents a specific sensitivity (recall) and specificity (1 – FPR) trade-off. A classifier that randomly guesses would produce a diagonal ROC line, whereas a perfect classifier reaches the top-left corner (TPR=1, FPR=0). The quality of a model’s ROC curve is often summarized by the AUC (Area Under the Curve) – the higher the AUC, the better the model is at ranking positives above negatives. ROC curves are especially useful for comparing classifiers and choosing operating points in imbalanced datasets or scenarios where the cost of false positives vs. false negatives varies.