<p>Out of bag estimates are used for estimating the error, I don't think you can switch to CV using that package. It's up to you to decide whether CV is better than this. In their <a href="https://cran.r-project.org/web/packages/tuneRanger/readme/README.html" rel="nofollow noreferrer">readme</a>, they linked to a <a href="https://arxiv.org/pdf/1804.03515.pdf" rel="nofollow noreferrer">publication</a>, and under it section 3.5 they wrote:</p>
<blockquote>
<p>Out-of-bag predictions are used for evaluation, which makes it much
faster than other packages that use evaluation strategies such as
cross-validation</p>
</blockquote>
<p>If you want to use cross-validation or repeated cross-validation, you would have to use <code>caret</code>, for example:</p>
<pre><code>library(caret)

mdl = train(Species ~ .,data=iris,method=&quot;ranger&quot;,trControl=trainControl(method=&quot;repeatedcv&quot;,repeats=2),
tuneGrid = expand.grid(mtry=2:3,min.node.size = 1:2,splitrule=&quot;gini&quot;))

Random Forest 

150 samples
  4 predictor
  3 classes: 'setosa', 'versicolor', 'virginica' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 2 times) 
Summary of sample sizes: 135, 135, 135, 135, 135, 135, ... 
Resampling results across tuning parameters:

  mtry  min.node.size  Accuracy  Kappa
  2     1              0.96      0.94 
  2     2              0.96      0.94 
  3     1              0.96      0.94 
  3     2              0.96      0.94 

Tuning parameter 'splitrule' was held constant at a value of gini
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were mtry = 2, splitrule = gini
 and min.node.size = 1.
</code></pre>
<p>The parameters you can tune will be different. I think <code>mlr</code> also allows you to perform <a href="https://mlr.mlr-org.com/articles/tutorial/resample.html" rel="nofollow noreferrer">cross-validation</a> but the same limitations apply.</p>
Repeated CV in TuneRanger
<p>I am using the package &quot;TuneRanger&quot; to tune a RF model. It works good and I obtained good results but I am not sure if it is overfitting my model. I would like to use a Repeated CV for every instance the package is tuning the model but I can't find a way to do it. Also I would like to know if anybody knows how the package validates the results of every try (train-test, cv, repeated cv?) I have been reading the instructions of the package (<a href="https://cran.r-project.org/web/packages/tuneRanger/tuneRanger.pdf" rel="nofollow noreferrer">https://cran.r-project.org/web/packages/tuneRanger/tuneRanger.pdf</a>) but it says nothing about it.</p>
<p>Thank you for your help.</p>