caret createFolds() returnTrain = FALSE vs returnTrain = TRUE - r

Question

I'm trying to understand the relationship between returnTrain = TRUE versus returnTrain = FALSE and the use of index/indexOut in the trainControl object.
My question: Is it possible to use returnTrain = TRUE and returnTrain = FALSE in such a way that we obtain equivalent model results?
I think I understand the behavior of caret::createFolds(). If we use createFolds(returnTrain = FALSE), we get a list of observation indices that are held out from each fold. When I see people do this, they usually assign these folds to the index() argument in the trainControl() object.
If we use createFolds(returnTrain = TRUE), we get the index of observations included in each fold. This is where I'm hazy. Should these folds be assigned to index or indexOut? Or should I be using both index and indexOut with returnTrain = TRUE?
In the first case, I am using returnTrain = FALSE and assigning myFolds to index
library(liver)
library(caret)
library(dplyr)
data(churn)
head(churn)
churn_data <- churn
# minority = yes; majority = no
churn_data %>% group_by(churn) %>% count()
set.seed(1994)
train.index <- createDataPartition(churn_data$churn, p = 0.8, list = FALSE)
train_churn <- churn_data[train.index,]
test_churn <- churn_data[-train.index,]
myFolds <- createFolds(train_churn$churn, k = 5, returnTrain = FALSE)
myControl <- trainControl(
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = TRUE,
index = myFolds,
sampling = "up")
set.seed(1994)
model_rf <- train(churn ~ .,
data = train_churn,
method = "rf",
ntree = 100,
tuneLength = 10,
metric = "ROC",
maximize = TRUE,
trControl = myControl)
model_rf
Then I use returnTrain = TRUE and assign these myFolds_two to index. The results are clearly different. So, I probably not using the index/indexOut arguments correctly.
myFolds_two <- createFolds(train_churn$churn, k = 5, returnTrain = TRUE)
myControl_two <- trainControl(
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = TRUE,
index = myFolds_two,
sampling = "up",
method = "cv")
set.seed(1994)
model_rf_two <- train(churn ~ .,
data = train_churn,
method = "rf",
ntree = 100,
tuneLength = 10,
metric = "ROC",
maximize = TRUE,
trControl = myControl_two)
model_rf_two
Can anyone shed light on this?