Because in many fields, linear models (even poor ones) are the best we're going to get, with more complex models losing to overfitting.

That's privileging a particular class of models just because they historically were easy to calculate.

If you're concerned about overfitting you need to be careful with how many parameters are you using, but that does not translate into an automatic advantage of a linear model over, say, a log one.

The article you linked to goes to pre-(personal)computer times when dealing with non-linear models was often just impractical.