Conceptual clustering is a machine learning technique that organizes data into meaningful categories by taking into account the underlying concepts of the data points, rather than just their statistical properties. It enhances the interpretability of clusters by generating human-understandable labels or descriptions for each cluster based on shared attributes and inherent relationships.