diff --git a/models/files/glmnet.R b/models/files/glmnet.R
index 4650971b9..85106b8b4 100644
--- a/models/files/glmnet.R
+++ b/models/files/glmnet.R
@@ -134,4 +134,10 @@ modelInfo <- list(label = "glmnet",
                   tags = c("Generalized Linear Model", "Implicit Feature Selection", 
                            "L1 Regularization", "L2 Regularization", "Linear Classifier",
                            "Linear Regression"),
-                  sort = function(x) x[order(-x$lambda, x$alpha),])
+                  sort = function(x) x[order(-x$lambda, x$alpha),],
+                  trim = function(x) {
+                    x$call <- NULL
+                    x$df <- NULL
+                    x$dev.ratio <- NULL
+                    x
+                  })
diff --git a/pkg/caret/inst/models/models.RData b/pkg/caret/inst/models/models.RData
index 453da3f38..e966abc33 100644
Binary files a/pkg/caret/inst/models/models.RData and b/pkg/caret/inst/models/models.RData differ
diff --git a/pkg/caret/tests/testthat/trim_glmnet.R b/pkg/caret/tests/testthat/trim_glmnet.R
new file mode 100644
index 000000000..edba16e97
--- /dev/null
+++ b/pkg/caret/tests/testthat/trim_glmnet.R
@@ -0,0 +1,57 @@
+library(caret)
+
+test_that('glmnet classification', {
+  skip_on_cran()
+  set.seed(1)
+  tr_dat <- twoClassSim(200)
+  te_dat <- twoClassSim(200)
+
+  set.seed(2)
+  class_trim <- train(Class ~ ., data = tr_dat,
+                      method = "glmnet",
+                      tuneGrid = data.frame(lambda = .1, alpha = .5),
+                      trControl = trainControl(method = "none",
+                                               classProbs = TRUE,
+                                               trim = TRUE))
+
+  set.seed(2)
+  class_notrim <- train(Class ~ ., data = tr_dat,
+                        method = "glmnet",
+                        tuneGrid = data.frame(lambda = .1, alpha = .5),
+                        trControl = trainControl(method = "none",
+                                                 classProbs = TRUE,
+                                                 trim = FALSE))
+
+  expect_equal(predict(class_trim,   te_dat),
+               predict(class_notrim, te_dat))
+
+  expect_equal(predict(class_trim,   te_dat, type = "prob"),
+               predict(class_notrim, te_dat, type = "prob"))
+
+  expect_less_than(object.size(class_trim)-object.size(class_notrim), 0)
+})
+
+test_that('glmnet regression', {
+  skip_on_cran()
+  set.seed(1)
+  tr_dat <- SLC14_1(200)
+  te_dat <- SLC14_1(200)
+
+  set.seed(2)
+  reg_trim <- train(y ~ ., data = tr_dat,
+                    method = "glmnet",
+                    tuneGrid = data.frame(lambda = .1, alpha = .5),
+                    trControl = trainControl(method = "none",
+                                             trim = TRUE))
+
+  set.seed(2)
+  reg_notrim <- train(y ~ ., data = tr_dat,
+                      method = "glmnet",
+                      tuneGrid = data.frame(lambda = .1, alpha = .5),
+                      trControl = trainControl(method = "none",
+                                               trim = FALSE))
+  expect_equal(predict(reg_trim,   te_dat),
+               predict(reg_notrim, te_dat))
+  expect_less_than(object.size(reg_trim)-object.size(reg_notrim), 0)
+})
+