MLM vs. MLMVS estimators in lavaan

I am conducting SEM with R lavaan package.

When trying to estimate model parameters with:

estimator="MLM" (ML with robust standard errors and a Satorra-Bentler scaled test statistic)

and

estimator="MLMVS" (ML with robust standard errors and a mean- and variance adjusted test statistic)

I receive the exact same output, including the same chi-square and test statistic value.

What might be the reason?

R code below:

data_selected<-as.data.frame(read_sav("final.sav"))[14:20]

//define model

FullModel=

"

//Latent variable definition

IWB =~ aNOVE+bEXPL+cTEST+dSUPP+e*IMPL

//Direct effects

IWB <~ f*OSE

//Medation

IWB <~ g*WE

WE ~ h*OSE

//Indirect effect

gh:=g*h

//Total effect

total:= a+b+c+d+e+f+(g*h)

//Covariances

NOVE ~~ EXPL + SUPP + IMPL

EXPL ~~ TEST + SUPP + IMPL

TEST ~~ SUPP + IMPL

SUPP ~~ IMPL

"

measurementModel<- cfa(FullModel, data=data_selected, std.lv=TRUE, estimator="MLM")

summary(measurementModel, standardized=T, fit.measures=TRUE)