Example Concept: Accuracy is suitable for balanced datasets where all classes are equally important. Precision is crucial when the cost of false positives is high, while recall is important when false negatives are more costly. The F1-score balances precision and recall, making it useful for imbalanced datasets. AUC-ROC provides a threshold-independent measure of a model's ability to distinguish between classes, ideal for comparing models across different thresholds.