The Silhouette Method is a technique used to determine the optimal number of clusters ($k$) in K-means clustering by calculating the silhouette coefficient for each data point. The silhouette coefficient measures how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The optimal $k$ is the value that maximizes the average silhouette score across all data points.