I think you would use MLComp as a baseline. You have a set of data that you can run generic algorithms on to show you what the best results are without any further considerations.

It is also useful for researchers who develop new algorithms for a specific dataset but then make it a generic algorithm. No research has the time to sit there manually testing every dataset that they can find to see what their algorithm works well on.

So for data set providers it gives them a quick look at what machine learning can offer without a lot of development.

For researchers it gives them a chance to see a surprising result, maybe their algorithm works well on a dataset they never considered.