Model comparison with CV using standard error
It is possible, however, this is usually not of great concern. If you would like to take into account the standard error, one possibility is the following: 


You compute the standard error of the best performing model (using your amelioration metric)
Consider all models that perform within one standard error (or less if you prefer) 
Select the model with the simplest decision boundary 


This is a heuristic(!) based on Occam's razor that simpler models tend to generalize better. However, keep in mind that for complicated problems simpler models are probably just that: simpler. They don't have to be more accurate. 

Alternatively, instead of 3. you can select the model with the smallest standard error, if your main goal is to limit your exposure to this kind of variability. 

Also keep in mind that there is No Unbiased Estimator of the Variance of K-Fold Cross-Validation, so this method brings with it its own uncertainty.

In general, I would just keep an eye on the standard error and if it's not too concerning just go with the numerically best-performing model.