Ward's Method is a hierarchical clustering technique that minimizes the total within-cluster variance by merging clusters that result in the smallest increase in the total sum of squared deviations. It is particularly useful for creating compact and well-separated clusters, making it a popular choice in data analysis applications where interpretability and accuracy are crucial.