Something I have been noticing lately, is that Machine Learning practitioners appear to be talking more about Cross Validation a lot more than those working with traditional statistical models (e.g. Regression).
For example, in almost any Machine Learning paper, the results section usually contains an extensive Cross Validation section where the Machine Learning models (e.g. Random Forest, Gradient Boosting, Neural Networks) are evaluated against testing and validation sets. As a matter of fact, emerging and key concepts in Machine Learning such as "Double Descent" (e.g. https://openai.com/blog/deep-double-descent/) are rooted in the milieu of Cross Validation.
On the other hand, I don't see Cross Validation being used as extensively in Statistics papers (e.g. within Epidemiology). In many papers, I see that the researchers might fit a Regression model to their data - and then report on the quality of the "model fit" and the statistical significance of the regression coefficients; but they do not seem to be engaging in Cross Validation as such (i.e. building their model on a random subset of the data).
I have researchers make arguments that modern Cross Validation has its roots purely in Statistics. The theoretical framework of Cross Validation is largely borrowed from concepts such as Bootstrap Sampling in which we assert that statistics calculated on multiple random samples from a population can converge to the true value of this statistics.
However, I am still not sure as to why Cross Validation appears to be more popular in Machine Learning compared to Statistics.
Can someone please comment on this?