This is a very fundamental question but I want to make sure I get this right.
K-fold cross validation will only help in predicting the accuracy and other metrics of the model but not really improve the model. Is that correct?
I am trying to read and learn about it and what I find is this is the approach mainly to have a better estimate of the model performance than applying it on single data set. However, CV cannot directly help in model improvement as in it will not give out a better model. You would have to tune the model again through different algorithms and again run it through CV to compare performance.
Is my understanding correct?