Isolation Forests effectively detect anomalies, leveraging tree-based structures to isolate outliers efficiently. Their implementation in Python provides a versatile tool for identifying anomalies in various datasets. However, users should be mindful of limitations such as sensitivity to data dimensionality and potential bias towards smaller clusters.