The blue crosses are the data. The red line is the line of best fit. The black line is a polynomial of degree 50 of best fit. High dimensional models have a tendency to fit the data by wiggling wildly.
That problem would be handled by cross-validation; the OP is saying that a simple model doesn’t have an obvious advantage assuming that both validate.
Given that both models validate, the main reason to prefer a simpler model is the sort of thing in Gears vs Behavior: the simpler model is more likely to contain physically-realistic internal structure, to generalize beyond the testing/training sets, to handle distribution shifts, etc.
Here is why you use simple models.
The blue crosses are the data. The red line is the line of best fit. The black line is a polynomial of degree 50 of best fit. High dimensional models have a tendency to fit the data by wiggling wildly.
That problem would be handled by cross-validation; the OP is saying that a simple model doesn’t have an obvious advantage assuming that both validate.
Given that both models validate, the main reason to prefer a simpler model is the sort of thing in Gears vs Behavior: the simpler model is more likely to contain physically-realistic internal structure, to generalize beyond the testing/training sets, to handle distribution shifts, etc.
It depends on what cross validation you are using. I would expect complex models to rarely cross validate.