K-Means Clustering works by initializing 'k' centroids randomly and then iteratively refining these centroids by assigning each data point to the nearest centroid, recalculating the centroids based on the assigned points, and repeating the process until convergence. The algorithm requires you to specify the number of clusters 'k' beforehand.