Why to adjust class weights instead of simply finding the best threshold?
If there are enough samples of both classes, I don't think it makes a lot of sense. I've been in kaggle competitions with very imbalanced datasets, like:

Fraud detection
Home default risk
Quora insincere questions

And none of the top solutions used any kind of treatment of the imbalance, as there were enough samples for both classes.
I've also done myself, several fraud-related models with high imbalance, and using no imbalance solution proved better than all the other options.