The ROC curve is a graphical plot used to evaluate the performance of a binary classification model by illustrating the trade-off between the true positive rate and the false positive rate at various threshold settings. A model with a curve closer to the top-left corner indicates better performance, with the area under the curve (AUC) being a single scalar value summarizing the overall ability of the model to distinguish between classes.