ROC curves are crucial because they provide a comprehensive measure of a test's performance. They help in understanding how well a diagnostic test or model discriminates between two conditions, such as diseased vs. non-diseased individuals. Unlike other metrics, ROC curves offer a visual and quantitative method to compare different tests or models independent of disease prevalence.
