Text clustering is an unsupervised machine learning technique used to group a set of documents into clusters based on their content similarity, facilitating efficient information retrieval and organization. It is widely applied in areas such as topic modeling, document classification, and data mining to uncover hidden patterns and insights from large textual datasets.