K-means is an unsupervised machine learning algorithm used for clustering data into distinct groups based on feature similarity, optimizing the partition by minimizing the variance within each cluster. It iteratively refines cluster centroids and assigns data points to the nearest centroid until convergence, making it efficient for large datasets but sensitive to initial centroid placement and the choice of k, the number of clusters.