I think this is an essential, foundational skill for modern data science, not unlike some knowledge of R or Python, but an order of magnitude or two harder to learn. A lot of the difficulty is in lack of good educational material. I also think this is significantly more important than a deep understanding of various modeling approaches; you can learn that as you go, so long as you know if what you have now is "good enough" or if you need a fancier approach.
Unfortunately you can't just throw k-fold CV around, average some fit metrics, and call it a day.
You want a better employee than your competitors have. You'll take all the expertise you can get, plus the ability to invent new techniques as needed. Everybody wants the very best 'cause that last 1% of skill can still be worth a lot of money. Everybody wants someone who can hit a lot of long balls for their team, as it were. There might be a floor on the skill level you could make some use of, but there's no ceiling.
My experience has been that this doesn't matter in a lot of cases. A product can be both, highly useful and subtly broken.
One of the biggest boons to my productivity was realizing when something is good enough and to move on. You can waste so much time tuning for precision and recall.