K-means clustering is a popular unsupervised machine learning algorithm used to partition a dataset into K distinct clusters. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids until convergence. K-means is widely employed in data mining, pattern recognition, and customer segmentation due to its simplicity and efficiency in handling large datasets.