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Disentangling Machine Learning Theory with Cross-Validation

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Does anyone see a link between machine learning's repeated epochs of training and the concept of cross-validation in linear modeling theory?

This article demonstrates what I perceive to be confusion about the validity of cross-validation combined with Bayesian optimization:

https://piotrekga.github.io/Pruned-Cross-Validation/

I am starting to believe cross-validation is actually a slower, less effective approximation of Bayesian inference. That opinion is informed by this Biometrika article from earlier this year (although I do not fully agree with the theoretical framework of coherence and prefer a more Jaynesian approach, but unfortunately he has been dead for more than 20 years):

https://academic.oup.com/biomet/article/107/2/489/5715611

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