Hello,
To begin with I would recommend reading a little about what cross-validation stands for from here:
The documentation for kfoldLoss
says that L is "The loss (mean squared error) between the observations in a fold when compared against predictions made with a tree trained on the out-of-fold data. If mode is 'individual', L is a vector of the losses. If mode is 'average', L is the average loss."
This post has a mathematical representation of the aforementioned statement:
Therefore, the way I see it, the value of 536 indicates that the average mean squared error over the cross validation sets for your model is 536.
If you would like to see the individual loss values corresponding to each of the partitioned data sets, you can set the 'mode' property for kfoldLoss to be 'individual'.