The Receiver Operating Characteristic (ROC) 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 two classes. ROC curves are useful for comparing different models or classifiers. The area under the ROC curve (AUC) quantifies the overall ability of the model to discriminate between the positive and negative classes. A higher AUC indicates better model performance, making it a valuable tool in fields like medicine, machine learning, and data science.