It might be hard to tell, but the second model ran faster than the first one. Because this dataset is relatively small, the difference in run times is probably only a few seconds; but for larger real-world datasets, the difference might be very noticeable—minutes or even hours. Using feature selection may speed up your processing times dramatically.

The second tree also contains fewer tree nodes than the first. It's easier to comprehend. Using fewer predictors is less expensive. It means that you have less data to collect, process, and feed into your models. Computing time is improved. In this example, even with the extra feature selection step, model building was faster with the smaller set of predictors. With a larger real-world dataset, the time savings should be greatly amplified.

Using fewer predictors results in simpler scoring. For example, you might identify only four profiles of customers who are likely to respond to the promotion. Note that with larger numbers of predictors, you run the risk of overfitting your model. The simpler model may generalize better to other datasets (although you would need to test this to be sure).

You could instead use a tree-building algorithm to do the feature selection work, allowing the tree to identify the most important predictors for you. In fact, the CHAID algorithm is often used for this purpose, and it's even possible to grow the tree level-by-level to control its depth and complexity. However, the Feature Selection node is faster and easier to use. It ranks all of the predictors in one fast step, allowing you to identify the most important fields quickly.