# Cross-validation

Hi, I'm deploying machine learning models in my MSc thesis using Weka. I have noticed that when I use 10-fold cross-validation in the training dataset I get low evaluation metrics compared to training the model on the entire dataset without cross-validation, this can make sense if I get better performance for the model when I test it on the testing dataset, however, it keeps the same performance. For example, I trained a random forest model for a regression problem and these are the results I have got:

## 1. Without cross-validation:

### Training:

R = 0.97; Mae = 1.31; Rmse = 1.78

### Testing:

R = 0.91; Mae = 2.70; Rmse = 3.57

## 2. With 10-fold cross-validation:

### Training:

R = 0.76; Mae = 3.55; Rmse = 4.77

### Testing:

R = 0.91; Mae = 2.70; Rmse = 3.57

As you can see the performance of the model on the testing dataset is the same, can someone explain to me what is the purpose of using it if the model is the same in either case?