An ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. The area under the ROC curve (AUC) quantifies the overall ability of the test to discriminate between the two conditions.
