Demo of how to use grid search on H2O's XGBoost: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html

h2o_xgboost_grid_example.R

library(h2o)
h2o.init()


# Load the HIGGS dataset
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")
y <- "response"
x <- setdiff(names(train), y)
family <- "binomial"

#For binary classification, response should be a factor
train[,y] <- as.factor(train[,y])
test[,y] <- as.factor(test[,y])

# Some XGboost/GBM hyperparameters
hyper_params <- list(ntrees = seq(10, 1000, 1),
                     learn_rate = seq(0.0001, 0.2, 0.0001),
                     max_depth = seq(1, 20, 1),
                     sample_rate = seq(0.5, 1.0, 0.0001),
                     col_sample_rate = seq(0.2, 1.0, 0.0001))
search_criteria <- list(strategy = "RandomDiscrete",
                        max_models = 10, 
                        seed = 1)

# Train the grid
xgb_grid <- h2o.grid(algorithm = "xgboost",
                     x = x, y = y,
                     training_frame = train,
                     nfolds = 5,
                     seed = 1,
                     hyper_params = hyper_params,
                     search_criteria = search_criteria)


# Sort the grid by CV AUC
grid <- h2o.getGrid(grid_id = xgb_grid@grid_id, sort_by = "AUC", decreasing = TRUE)
grid_top_model <- grid@summary_table[1, "model_ids"]

Thanks rknimmakayala,
thats's a little bit to much for me. I do it native in r via caret grid search. Works like a charme.

set.seed(42)
        # xgboost train as.matrix + grid.search...
        # https://www.r-bloggers.com/r-setup-a-grid-search-for-xgboost/
        bootControl <- trainControl(number=4, verboseIter=TRUE)

        # set up the cross-validated hyper-parameter search
        xgb_grid_1 = expand.grid(
            eta = c(0.01, 0.001, 0.0001),
            max_depth = c(2, 4, 6, 8, 10, 15, 45),
            gamma = 1,
            nrounds = 1400,
            colsample_bytree = 1,    #default=1
            min_child_weight = 1,     #default=1
            subsample = 1
        )
        
        # pack the training control parameters
        xgb_trcontrol_1 = trainControl(
            method = "repeatedCV",
            number = 15,
            verboseIter = TRUE,
            returnData = FALSE,
            returnResamp = "all",# save losses across all models
            #classProbs = TRUE,# set to TRUE for AUC to be computed
            summaryFunction = twoClassSummary,
            allowParallel = TRUE,
            savePred="final",
            repeats=5
        )
    
        # train the model for each parameter combination in the grid,
        modFit = train(
            #x = as.matrix(f),
            #y = as.factor(y),
            #x = as.matrix(f),
            #y = as.factor(y),
            f, 
            data=training,
            #data=as.matrix(training),
            trControl = xgb_trcontrol_1,
            tuneGrid = xgb_grid_1,
            method = "xgbTree",
            metric=metric
        )