No its not.
Its analogous to quantifying an F test with watts.
Nothing wrong with that. I can give a billion row dataset to a dozen people. John assumes normality, samples 100 rows, runs a linear regression, gets 70% R^2, under 1 second on a 1980s era computer. Mary doesn't assume normality, runs a glm via irwls, gets better explanatory power than John, still on 1980s computer, though she takes 5 seconds instead of 1. Then James comes along & runs a decision tree & is twice as good as Mary, but he now needs a second on a 1990s PC. Tony uses a random forest & Baker wants a 128 layer neural net. And so on & so forth, until we end up with burning enough energy that would otherwise power a village, to overfit some dataset that excel at some completely artificial leaderboard metric.
At some point a grown up comes in & says you don't need to light up your bedroom with industrial stadium lighting if you need to do your homework, a 40w table lamp will suffice. Hell half the third world uses a candle & they are performing quite well, if not better. That's really all this is.
The marginal gains in performance aren't justified compared to the amount of energy you end up expending. Half these models are brittle & have zero shelf life, ppl regularly throw away stuff they wrote just a year ago, so to what raison d'etre this pursuit ?