AUC (Area Under the Receiver Operating Characteristic Curve) is a widely used metric in machine learning that measures the degree of separation between positive and negative classes in a dataset, calculated as the area under a staircase-like curve generated by varying threshold values for prediction scores. It's useful across various use-cases, particularly when models output scores, providing a single-number heuristic of how well a model can differentiate data points with true positive labels from those with true negative labels. AUC ranges from 0 to 1, where 1 indicates perfect separation and 0.5 suggests no separation, and it's often used in data science competitions and when accuracy is insufficient. However, AUC may not be the best metric for all problems, especially those involving probabilities or business outcomes, as it doesn't account for calibrated predicted probabilities or false positive rates. Ultimately, understanding the tradeoffs of using AUC and other model metrics is crucial for selecting the right metric to evaluate a model's performance in a specific context.