The "Area Under Curve" (AUC) is a statistical measure used to evaluate the performance of a model, particularly in binary classification tasks. It represents the degree to which a model can distinguish between two classes. AUC values range from 0 to 1, where 0.5 indicates no discrimination (similar to random guessing) and 1.0 signifies perfect discrimination. In the context of Receiver Operating Characteristic (ROC) curves, the AUC quantifies the overall ability of the model to correctly classify positive and negative instances across various threshold settings. A higher AUC value indicates a better-performing model, making it a valuable metric in fields like machine learning and medical diagnostics.