An ROC (Receiver Operating Characteristic) Curve is a graphical plot illustrating the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. The area under the ROC curve (AUC) is a measure of the classifier's performance; a higher AUC indicates a better ability to distinguish between positive and negative classes. ROC curves are widely used to evaluate the performance of classification models, particularly in medical diagnostics, fraud detection, and other applications where distinguishing between two classes is critical.