K-Means clustering is an unsupervised machine learning algorithm used to partition a dataset into K distinct, non-overlapping subsets or clusters based on feature similarity. It iteratively assigns data points to clusters by minimizing the variance within each cluster, making it effective for exploratory data analysis and pattern recognition tasks.