Hierarchical clustering is an unsupervised learning method used to build a hierarchy of clusters by either iteratively merging smaller clusters into larger ones (agglomerative) or dividing larger clusters into smaller ones (divisive). It is particularly useful for visualizing data structure and relationships through dendrograms, allowing for flexible and interpretable clustering solutions without needing to pre-specify the number of clusters.