K-means clustering is a popular tool for a type of learning called unsupervised learning. This is because it's simple, efficient, and easy to use. Here’s why many data scientists like it:

1. Simplicity: K-means is easy to understand. You begin by choosing a number of groups, called $k$ clusters. Then, the algorithm works by assigning each data point to the closest cluster and updating the center point of each cluster.

2. Efficiency: K-means can quickly handle a lot of data. It is not very heavy on computer resources, which makes it great for large sets of information. The time it takes is based on three things: the total number of data points ($n$), the number of clusters ($k$), and how many times the algorithm runs ($i$).

3. Flexibility: This method works well with different kinds of data. For example, when looking at customer information, K-means can help find different groups based on what people buy.

4. Scalability: It can manage big sets of data without slowing down, which is super important in today’s world where data is everywhere.

Because of these benefits, K-means is a popular choice for many tasks, like grouping customers in the market or reducing the size of images.