The Precision-Recall Curve is a graphical representation used to evaluate the performance of a classification model, particularly in scenarios with imbalanced datasets. It plots two metrics: Precision, which measures the accuracy of positive predictions, and Recall, which indicates the model's ability to identify all relevant instances. This curve helps visualize the trade-off between precision and recall at different threshold settings. A model with high precision and recall will have a curve that approaches the top-right corner of the plot, indicating effective performance in identifying true positives while minimizing false positives.