The Elbow Method is a commonly used technique in data science and machine learning to determine the optimal number of clusters in a dataset. It is particularly useful in K-means clustering, where the goal is to partition the data into a predefined number of clusters. The method involves plotting the explained variation as a function of the number of clusters and finding the 'elbow point' where the rate of decrease sharply shifts. This point is considered as the optimal number of clusters.
