I don’t know much about the metrics for model selection, but do know that various statisticians argue against Bayes factors (Tendeiro & Kiers, 2019; Gelman et al., 2020).
As far as I understand, McElreath (2020) and Gelman et al. (2020) advise to do model comparison based on predictive performance measures such as cross-validation and AIC. McElreath (2020) argues that these metrics are quite similar.
But, I think that it also depends on where you want to publish. My co-authors aren’t comfortable with cross-validation, so I will stick to a multiverse analysis (Steegen et al., 2016) with a multilevel model to reduce overfitting and clear graphs to show what the model does.