K-means has a lot going for it, but it's not very good at recovering "natural" clusters. Like if you have a dataset that you know actually is grouped in a certain way, k-means very often will not discover those clusters. Other more complex clustering algorithms will do a more reliable job. If you have to implement clustering yourself for some reason, k-means' simplicity is great. But how often do you need to write your own algorithm these days?