AUC, or Area Under the Curve, is a performance measurement for classification models, particularly in binary classification tasks. It quantifies the ability of a model to distinguish between positive and negative classes. The AUC value ranges from 0 to 1, where a value of 0.5 indicates no discrimination (similar to random guessing), and a value of 1 signifies perfect classification. In the context of Receiver Operating Characteristic (ROC) curves, AUC represents the area under the curve plotted between the true positive rate and false positive rate. A higher AUC value indicates a better-performing model, making it a valuable metric in fields like machine learning and medical diagnostics.