AUC (Area Under the Precision-Recall Curve) is a metric used to evaluate the performance of a classifier, particularly useful for imbalanced datasets where the positive class is rare. It measures the degree of separation between positive and negative classes based on their prediction scores. A perfect PR curve would have an AUC of 1, indicating ideal performance. However, PR AUC fails if teams misclassify the positive class or don't consider True Negatives equally with False Positives. In contrast to ROC AUC, which is more suitable for balanced datasets, PR AUC emphasizes precision and recall on the positive class, making it a better choice for tasks like disease diagnosis or fraud detection where identifying minority events is crucial. Understanding the tradeoffs of different metrics is essential when optimizing model performance.