I think you are mistaking what K-fold cross validation is for: remember the difference between the process of training a model which returns a model (trained, hopefully) and the process of evaluating a model, which returns an evaluation of a model (a number usually).
You can dig into K-fold briefly by reading this post 10.
Basically you use K-fold CV to evaluate the performance of a model. The final performance is the mean performance (± standard deviation) over all the folds.
If you want to obtain the final trained model you can retrain from scratch your model on all your training set (without K-split) and the evaluation you have done will tell you the expected performance on the separate test set.
Maybe also this link can clarify:

  
      stats.stackexchange.com
  
  
      
    
  

  How to choose a predictive model after k-fold cross-validation? 14



  cross-validation, model-selection



  asked by
  
  
    Berk U.
  
  on 02:21AM - 15 Mar 13 UTC