Using standard deviation as a metric for model selection
Does the validation score's standard deviation have a correlation with overfitting / error 


Yes definitely: a high variance shows that the model is not stable across different training sets, which indicates a high risk of overfitting.


  and can this be used in my scoring? 


Using the std dev directly in the scoring itself, I'm not sure. I would consider this more like a kind of qualitative indication. However if two different models have  similar performance but one with higher variance than the other, it's usually a good idea to select the latter.


  currently the stdev is around 5% that seems ALOT.


It always depends on the specifics of the data, but yes I'd say that 5% is quite high.


  I propose that if I reduce the stdev of the validation scores of each k fold then this should decrease variance on out of sample predictions? 


I don't understand what you mean here.

In general the standard way to reduce overfitting is to increase the ratio between number of instances and number/complexity of the features:


Add more instances in the training set if possible (but it's rarely possible)
Apply feature selection to reduce the number of features
Simplify the most complex features; that depends on the task, but typically removing values which appear very rarely can help.