Go beyond a surface-level understanding of KMeans. This guide provides a complete breakdown of the algorithm, starting with a practical look at tuning key Scikit-learn hyperparameters like n_clusters and init. We then dive into the crucial concepts of time and space complexity to understand how KMeans performs on large datasets. Finally, we explore the core mathematical objective, the challenges of finding an optimal solution, and how Lloyd's Algorithm works in practice.