Initial centroid selection is a crucial step in clustering algorithms such as k-means, as it can significantly affect the convergence speed and the quality of the final clusters. Effective strategies for selecting initial centroids, like k-means++, aim to minimize the risk of poor clustering results by ensuring a more strategic spread of initial points across the data space.