Linear regression : The value of R2 increasing with the incraese of the number of K folds when using cross validation : is it a good thing? Let's say I have a dataframe with one dependent continuous variable and multiple independent categorical and continuous variables. I want to apply linear regression (using R language in my case). The results include that : R2 = 0.45 and R2 adjusted = 0.44.
When applying the cross validation method to improve the predictibilty of the model I created, the more I increase the number of k folds, the more the value of R2 increases. Does that indicate a mistake I am making or it is just normal to have such a result and I have nothing to worry about ?
I have a reproducible example that I can upload here, but I am not sure if I can do that here on this platform.

That sounds like a reasonable result. Increasing the number of folds increases the size of the training set used for each fold. A model trained on a large training set will often be more accurate than one trained on a small training set, as it is less likely to overfit. Obviously there are exceptions - a highly biased model may not improve with a larger training set.
