The ROC Curve, or Receiver Operating Characteristic Curve, is a graphical representation used to evaluate the performance of a binary classification model. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. This helps in understanding how well the model distinguishes between the two classes. A key feature of the ROC Curve is the area under the curve (AUC), which quantifies the overall ability of the model to discriminate between positive and negative classes. An AUC of 1 indicates perfect classification, while an AUC of 0.5 suggests no discriminative power, similar to random guessing.