Unsupervised learning is a type of machine learning where the model is trained on data without explicit labels, allowing it to identify patterns and structures inherently present in the data. It is particularly useful for tasks such as clustering, dimensionality reduction, and anomaly detection, where the goal is to explore the underlying structure of the data rather than predict a specific outcome.